The CMMBS training program consists of integrated coursework and research, coupled with structured monitoring of mentoring and degree progress. See the proposal Program Plan for complete details.
Quantitative Biology Colloquium
The Quantitative Biology Colloquium (QBC) is the centerpiece of the training program, taken throughout all four funded semesters. A 1-unit course, it is required during each semester of funding. With presentations by students, faculty, and visiting speakers, it promotes dialog between trainees and faculty with primarily mathematical or computational backgrounds and those with strong biological training.
ECOL 519: Introduction to Modeling in Biology
Introduction to Modeling in Biology is a 3-credit course that trains PhD students to read modeling papers effectively and critically, assuming only familiarity with calculus concepts. The focus is on high level “input→black box→output” analysis approaches that enable students to grasp the key insights from and limitations of a model without getting lost in technical details. Biology students learn how biological questions can be addressed by models, even if implementation details in those models exceed their mathematical expertise. Quantitative students learn to look beyond implementation details to understand how models are applied in particular ways to a variety of biological problems.
MCB 547: Big Data in Molecular Biology and Biomedicine
Big Data in Molecular Biology and Biomedicine is a 3-credit course that gives students a broad and practical introduction to statistical and machine learning models applicable to modern large-scale biomedical data sets. The course assumes familiarity with introductory statistics and calculus. Throughout the course, students analyze a wide variety of real-world data, including human genotypes, RNA-seq counts, biomedical images, and electronic medical records. The course emphasizes not the bioinformatics necessary to generate each type of data, but rather analysis approaches that can be applied to many types of data. As a mathematical foundation, the course emphasizes the concept of a probability distribution, be it the high-dimensional distribution of data or the one-dimensional distribution of a test statistic.
Foundational Open Science Skills
The 12-week Foundational Open Science Skills (FOSS) workshop is a hands-on modular training program that teaches core competencies in open science, including command-line computing, version control with Git and GitHub, FAIR data principles, data hygiene and governance, reproducible workflows, and cloud-based analysis using CyVerse’s national cyberinfrastructure. While technically rigorous, the curriculum is designed for accessibility, welcoming participants with little to no coding experience and guiding them toward proficiency in modern cloud-based, computational research practices. The curriculum was developed by CyVerse and the U of A Data Science Institute to equip early-career scientists with the practical skills required to thrive in today’s collaborative, computational research environment.